341 research outputs found
Decreasing the uncertainty of atomic clocks via real-time noise distinguish
The environmental perturbation on atoms is the key factor restricting the
performance of atomic frequency standards, especially in long term scale. In
this letter, we demonstrate a real-time noise distinguish operation of atomic
clocks. The operation improves the statistical uncertainty by about an order of
magnitude of our fountain clock which is deteriorated previously by extra
noises. The frequency offset bring by the extra noise is also corrected. The
experiment proves the real-time noise distinguish operation can reduce the
contribution of ambient noises and improve the uncertainty limit of atomic
clocks.Comment: 5 pages, 4 figures, 1 tabl
Deep Generative Imputation Model for Missing Not At Random Data
Data analysis usually suffers from the Missing Not At Random (MNAR) problem,
where the cause of the value missing is not fully observed. Compared to the
naive Missing Completely At Random (MCAR) problem, it is more in line with the
realistic scenario whereas more complex and challenging. Existing statistical
methods model the MNAR mechanism by different decomposition of the joint
distribution of the complete data and the missing mask. But we empirically find
that directly incorporating these statistical methods into deep generative
models is sub-optimal. Specifically, it would neglect the confidence of the
reconstructed mask during the MNAR imputation process, which leads to
insufficient information extraction and less-guaranteed imputation quality. In
this paper, we revisit the MNAR problem from a novel perspective that the
complete data and missing mask are two modalities of incomplete data on an
equal footing. Along with this line, we put forward a generative-model-specific
joint probability decomposition method, conjunction model, to represent the
distributions of two modalities in parallel and extract sufficient information
from both complete data and missing mask. Taking a step further, we exploit a
deep generative imputation model, namely GNR, to process the real-world missing
mechanism in the latent space and concurrently impute the incomplete data and
reconstruct the missing mask. The experimental results show that our GNR
surpasses state-of-the-art MNAR baselines with significant margins (averagely
improved from 9.9% to 18.8% in RMSE) and always gives a better mask
reconstruction accuracy which makes the imputation more principle
Electrochemical Parameter Identification for Lithium-ion Battery Sources in Self-Sustained Transportation Energy Systems
Lithium-ion battery (LIB) sources have played an essential role in
self-sustained transportation energy systems and have been widely deployed in
the last few years. To realize reliable battery maintenance, identifying its
electrochemical parameters is necessary. However, the battery model contains
many parameters while the measurable states are only the current and voltage,
inducing the identification inherently an ill-conditioned problem. A parameter
identification approach is proposed, including the experiment, model, and
algorithm. Electrochemical parameters are first grouped manually based on the
physical properties and assigned to two sequenced tests for identification. The
two tests named the quasi-static test and the dynamic test, are compressed on
time for practical implementation. Proper optimization models and a
sensitivity-oriented stepwise (SSO) optimization algorithm are developed to
search for the optimal parameters efficiently. Typically, the Sobol method is
applied to conduct the sensitivity analysis. Based on the sensitivity indexes,
the SSO algorithm can decouple the mixed impacts of different parameters during
the identification. For validation, numerical experiments on a typical NCM811
battery at different life stages are conducted. The proposed approach saves
about half the time finding the proper parameter value. The identification
accuracy of crucial parameters related to battery degradation can exceed 95\%.
Case study results indicate that the identified parameters can not only improve
the accuracy of the battery model but also be used as the indicator of the
battery SOH
300 GHz Dual-Band Channel Measurement, Analysis and Modeling in an L-shaped Hallway
The Terahertz (THz) band (0.1-10 THz) has been envisioned as one of the
promising spectrum bands for sixth-generation (6G) and beyond communications.
In this paper, a dual-band angular-resolvable wideband channel measurement in
an indoor L-shaped hallway is presented and THz channel characteristics at
306-321 GHz and 356-371 GHz are analyzed. It is found that conventional
close-in and alpha-beta path loss models cannot take good care of large-scale
fading in the non-line-of-sight (NLoS) case, for which a modified alpha-beta
path loss model for the NLoS case is proposed and verified in the NLoS case for
both indoor and outdoor L-shaped scenarios. To describe both large-scale and
small-scale fading, a ray-tracing (RT)-statistical hybrid channel model is
proposed in the THz hallway scenario. Specifically in the hybrid model, the
deterministic part in hybrid channel modeling uses RT modeling of dominant
multi-path components (MPCs), i.e., LoS and multi-bounce reflected paths in the
near-NLoS region, while dominant MPCs at far-NLoS positions can be deduced
based on the developed statistical evolving model. The evolving model describes
the continuous change of arrival angle, power and delay of dominant MPCs in the
NLoS region. On the other hand, non-dominant MPCs are generated statistically.
The proposed hybrid approach reduces the computational cost and solves the
inaccuracy or even missing of dominant MPCs through RT at far-NLoS positions
300 GHz Channel Measurement and Characterization in the Atrium of a Building
With abundant bandwidth resource, the Terahertz band (0.1~THz to 10~THz) is
envisioned as a key technology to realize ultra-high data rates in the 6G and
beyond mobile communication systems. However, moving to the THz band, existing
channel models dedicated for microwave or millimeter-wave bands are
ineffective. To fill this research gap, extensive channel measurement campaigns
and characterizations are necessary. In this paper, using a frequency-domain
Vector Network Analyzer (VNA)-based sounder, a measurement campaign is
conducted in the outdoor atrium of a building in 306-321 GHz band. The measured
data are further processed to obtain the channel transfer functions (CTFs),
parameters of multipath components (MPCs), as well as clustering results. Based
on the MPC parameters, the channel characteristics, such as path loss, shadow
fading, K-factor, etc., are calculated and analyzed. The extracted channel
characteristics and numerology are helpful to study channel modeling and guide
system design for THz communications.Comment: 5 pages, 2 figures. arXiv admin note: text overlap with
arXiv:2203.16745 by other author
306-321 GHz Wideband Channel Measurement and Analysis in an Indoor Lobby
The Terahertz (0.1-10 THz) band has been envisioned as one of the promising
spectrum bands to support ultra-broadband sixth-generation (6G) and beyond
communications. In this paper, a wideband channel measurement campaign in an
indoor lobby at 306-321 GHz is presented. The measurement system consists of a
vector network analyzer (VNA)-based channel sounder, and a directional antenna
equipped at the receiver to resolve multi-path components (MPCs) in the angular
domain. In particular, 21 positions and 3780 channel impulse responses (CIRs)
are measured in the lobby, including the line-of-sight (LoS), non-line-of-sight
(NLoS) and obstructed-line-of-sight (OLoS) cases. Multi-path propagation is
elaborated in terms of clustering results, and the effect of typical scatterers
in the indoor lobby scenario in the THz band is explored. Moreover, indoor THz
channel characteristics are analyzed in depth. Specifically, best direction and
omni-directional path losses are analyzed by invoking close-in and alpha-beta
path loss models. The most clusters are observed in the OLoS case, followed by
NLoS and then LoS cases. On average, the power dispersion of MPCs is smaller in
the LoS case in both temporal and angular domains, compared with the NLoS and
OLoS counterparts.Comment: 6 pages, 15 figure
DSS-o-SAGE: Direction-Scan Sounding-Oriented SAGE Algorithm for Channel Parameter Estimation in mmWave and THz Bands
Investigation of millimeter (mmWave) and Terahertz (THz) channels relies on
channel measurements and estimation of multi-path component (MPC) parameters.
As a common measurement technique in the mmWave and THz bands, direction-scan
sounding (DSS) resolves angular information and increases the measurable
distance. Through mechanical rotation, the DSS creates a virtual multi-antenna
sounding system, which however incurs signal phase instability and large data
sizes, which are not fully considered in existing estimation algorithms and
thus make them ineffective. To tackle this research gap, in this paper, a
DSS-oriented space-alternating generalized expectation-maximization
(DSS-o-SAGE) algorithm is proposed for channel parameter estimation in mmWave
and THz bands. To appropriately capture the measured data in mmWave and THz
DSS, the phase instability is modeled by the scanning-direction-dependent
signal phases. Furthermore, based on the signal model, the DSS-o-SAGE algorithm
is developed, which not only addresses the problems brought by phase
instability, but also achieves ultra-low computational complexity by exploiting
the narrow antenna beam property of DSS. Simulations in synthetic channels are
conducted to demonstrate the efficacy of the proposed algorithm and explore the
applicable region of the far-field approximation in DSS-o-SAGE. Last but not
least, the proposed DSS-o-SAGE algorithm is applied in real measurements in an
indoor corridor scenario at 300~GHz. Compared with results using the baseline
noise-elimination method, the channel is characterized more correctly and
reasonably based on the DSS-o-SAGE.Comment: 15 pages, 10 figures, 3 table
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